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Basic Stats

Two Eyes:
Template Creation Time (s): 0.57 ± 0.07
Search Time (s): 100 ± 1
FNIR (@ FPIR=0.01): 1.0000 ± 0.0005    (± 90% confidence)
FNIR (@ FPIR=0.001): 1.0000 ± 0.0005    (± 90% confidence)
Miss Rate @ Rank 1: 0.8301
Miss Rate @ Rank 10: 0.5911
Miss Rate @ Rank 100: 0.1009
Failure to Enroll (FTE) Rate: 0.000006
Dataset: Operational Dataset 4th pull (stats on OPS4 images)
Samples used: Both eyes
Enrolled Population: 500K people
Enrollment Method: Both (left and right) iris images per enrollment template
Single Eye:
FNIR (@ FPIR=0.01): 1.0000 ± 0.0002    (± 90% confidence)
FNIR (@ FPIR=0.001): 1.0000 ± 0.0002    (± 90% confidence)
Miss Rate @ Rank 1: 0.3738
Miss Rate @ Rank 10: 0.1013
Miss Rate @ Rank 100: 0.0120
Failure to Enroll (FTE) Rate: 0.000003
Dataset: Operational Dataset 4th pull (stats on OPS4 images)
Samples used: One eye
Enrolled Population: 1M irides (500K people)
Enrollment Method: One iris image per enrollment template

DET Accuracy

Core accuracy for the identification task can be characterized by Detection-error trade-off (DET) plots. Generally, curves lower down in a DET plot correspond to more accurate matchers. The plots are interactive through the use of the Plotly.js graphing library.

Two-eye Accuracy

Dataset: Operational Dataset 4th pull (stats on OPS4 images)
Samples used: Both eyes
Enrolled Population: 500K people
Enrollment Method: Both (left and right) iris images per enrollment template

Single Eye Accuracy

Dataset: Operational Dataset 4th pull (stats on OPS4 images)
Samples used: One eye
Enrolled Population: 1M irides (500K people)
Enrollment Method: One iris image per enrollment template


CMC Curves

Two-eye Accuracy

Dataset: Operational Dataset 4th pull (stats on OPS4 images)
Samples used: Both eyes
Enrolled Population: 500K people
Enrollment Method: Both (left and right) iris images per enrollment template

Single-eye Accuracy

Dataset: Operational Dataset 4th pull (stats on OPS4 images)
Samples used: One eye
Enrolled Population: 1M irides (500K people)
Enrollment Method: One iris image per enrollment template


Demographics


Twins Dataset


Enrollment Size

Accuracy is impacted by the size of the enrollment database (a.k.a the gallery size). Identification of the correct mate is expected to be more difficult for larger enrollment database sizes. The figure below plots FNIR (at FPIR=\(0.01\)) as a function of enrollment database size.

Dataset: Operational Dataset 4th pull (stats on OPS4 images)
Accuracy Metric: FNIR (i.e., “miss rate”) at an FPIR of 0.01
Samples used: Both eyes
Enrollment Method: One enrollment session per person

Some apparant trends may be the result of random variation. Results for the 10K and 50K enrollment sizes were computed from 140K searches. Results for the 100K and 500K enrollment sizes were computed from 700K searches.

Contact Info

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